Transcript: 'Every's Head of Consulting Just Automated Her Job'

2026-02-03 · Source: AI & I - Every · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Project & Product Management · Depth: Intermediate, extended

Summary

Natalia Quintero, Head of Consulting at Every, discusses successful AI adoption strategies based on insights from over 100 companies. She highlights that effective AI integration requires a coordinated, top-down effort from leadership and the empowerment of internal AI champions. A private equity firm, for instance, reduced investment memo creation from weeks to 30 minutes by connecting AI to proprietary data and tailoring prompts to their specific investment thesis. For engineering teams, a "plan-delegate-assess-compound" framework proved crucial, enabling engineers to generate two weeks of work in an afternoon. Quintero also shared her personal experience building "Claudie," an AI project manager, which automates client onboarding and tracking, reducing her project management time from 10-15 hours to one hour weekly.

Key takeaway

For AI Product Managers evaluating enterprise AI solutions, prioritize those that enable deep integration with your company's unique data and workflows. Your success hinges on securing top-down leadership buy-in and fostering internal AI champions. Focus on solutions that allow for Savile Row prompt tailoring, as this precision is key to achieving significant time savings, such as reducing multi-week tasks to minutes, and freeing up your team for higher-value, human-centric work.

Key insights

Successful AI adoption demands top-down leadership, empowered champions, and tailored integration with proprietary context.

Principles

• AI adoption requires coordinated, top-down leadership.
• Empower internal champions to experiment and innovate with AI.
• Tailor AI solutions to specific organizational workflows and data.

Method

The "plan-delegate-assess-compound" framework enhances engineering team productivity with AI, focusing on structured planning before delegation, assessment, and compounding learnings.

In practice

• Automate investment memo drafting using AI with proprietary data.
• Develop AI agents for project management tasks like client onboarding.
• Dedicate creative exploration time outside core work hours for AI experimentation.

Topics

• AI Adoption Strategy
• Custom AI Agents
• Prompt Engineering
• Workflow Automation
• Claude Code

Best for: Executive, AI Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI & I - Every.